Applying Eye Tracking to Identify Autism Spectrum Disorder in Children
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Eye tracking (ET) holds potential for the early detection of autism spectrum disorder (ASD). To overcome the difficulties of working with young children, developing a short and informative paradigm is crucial for ET. We investigated the fixation times of 37 ASD and 37 typically developing (TD) children ages 4–6 watching a 10-second video of a female speaking. ASD children showed significant reductions in fixation time at six areas of interest. Furthermore, discriminant analysis revealed fixation times at the mouth and body could significantly discriminate ASD from TD with a classification accuracy of 85.1%, sensitivity of 86.5%, and specificity of 83.8%. Our study suggests that a short video clip may provide enough information to distinguish ASD from TD children.
KeywordsAutism Eye tracking Fixation time Machine learning Face
GW, XK, YL, JK contributes experimental design; GW, BS, YL, ZW, ZF contributes data collection; SY, YT, BS, MK contributes data analysis; JK, SY, GW, XK, YT, JP, CL contributes manuscript preparation.
This study was funded by Sanming Project of Medicine in Shenzhen (SZSM201512009). JK is supported by R01 AT008563, R21AT008707, and R61AT009310 from NIH/NCCIH.
Compliance with Ethical Standards
Conflict of interest
JK has a disclosure to report (holding equity in a startup company (MNT) and pending patents to develop new neuromodulation tools) but declares no conflict of interest. All other authors declare no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the Shenzhen Maternity & Child Healthcare Hospital research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from parents of all individual participants included in the study. We are grateful to Georgia J Wilson for her help in revising the manuscript.
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